Overview

Dataset statistics

Number of variables29
Number of observations406431
Missing cells0
Missing cells (%)0.0%
Duplicate rows413
Duplicate rows (%)0.1%
Total size in memory89.9 MiB
Average record size in memory232.0 B

Variable types

Numeric24
Categorical5

Alerts

Dataset has 413 (0.1%) duplicate rowsDuplicates
DIST_CIA is highly correlated with DIST_HECHOHigh correlation
DIST_HECHO is highly correlated with DIST_CIAHigh correlation
DPTO_CIA is highly correlated with DPTO_HECHO and 3 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
PROV_CIA is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
PROV_HECHO is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
REGION is highly correlated with DPTO_CIA and 1 other fieldsHigh correlation
TIPO is highly correlated with FEC_REGISTRO_ANIO and 1 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
DIST_CIA is highly correlated with DIST_HECHOHigh correlation
DIST_HECHO is highly correlated with DIST_CIAHigh correlation
DPTO_CIA is highly correlated with DPTO_HECHO and 3 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
PROV_CIA is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
PROV_HECHO is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
REGION is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
TIPO is highly correlated with REGION and 2 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
DIST_CIA is highly correlated with DIST_HECHOHigh correlation
DIST_HECHO is highly correlated with DIST_CIAHigh correlation
DPTO_CIA is highly correlated with DPTO_HECHO and 3 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
PROV_CIA is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
PROV_HECHO is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
REGION is highly correlated with DPTO_CIA and 1 other fieldsHigh correlation
TIPO is highly correlated with FEC_REGISTRO_ANIO and 1 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
TIPO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPOHigh correlation
COMISARIA is highly correlated with DIST_CIA and 3 other fieldsHigh correlation
DIRECCION is highly correlated with DIST_CIAHigh correlation
DIST_CIA is highly correlated with COMISARIA and 7 other fieldsHigh correlation
DIST_HECHO is highly correlated with COMISARIA and 5 other fieldsHigh correlation
DPTO_CIA is highly correlated with DIST_CIA and 5 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DIST_CIA and 5 other fieldsHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
MODALIDAD is highly correlated with SUB_TIPOHigh correlation
PROV_CIA is highly correlated with COMISARIA and 6 other fieldsHigh correlation
PROV_HECHO is highly correlated with COMISARIA and 6 other fieldsHigh correlation
REGION is highly correlated with DIST_CIA and 6 other fieldsHigh correlation
SUB_TIPO is highly correlated with MODALIDADHigh correlation
TIPO is highly correlated with REGION and 3 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
FEC_REGISTRO_ANIO is highly correlated with REGION and 2 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with TIPO and 1 other fieldsHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FEC_REGISTRO_DIA_SEM is highly correlated with FECHA_HORA_HECHO_DIA_SEMHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with TIPO and 1 other fieldsHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FECHA_HORA_HECHO_DIA_SEM is highly correlated with FEC_REGISTRO_DIA_SEMHigh correlation
DPTO_CIA has 4137 (1.0%) zeros Zeros
DPTO_HECHO has 4066 (1.0%) zeros Zeros
EST_CIVIL has 69121 (17.0%) zeros Zeros
REGION has 4806 (1.2%) zeros Zeros
VIA has 10550 (2.6%) zeros Zeros
FEC_REGISTRO_DIA_SEM has 70685 (17.4%) zeros Zeros
FECHA_HORA_HECHO_DIA_SEM has 62021 (15.3%) zeros Zeros

Reproduction

Analysis started2022-08-07 23:50:00.150617
Analysis finished2022-08-07 23:52:41.059727
Duration2 minutes and 40.91 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

COMISARIA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1041
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean490.0318701
Minimum0
Maximum1040
Zeros1942
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:41.135731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56
Q1224
median450
Q3787
95-th percentile997
Maximum1040
Range1040
Interquartile range (IQR)563

Descriptive statistics

Standard deviation306.1789456
Coefficient of variation (CV)0.6248143524
Kurtosis-1.243064837
Mean490.0318701
Median Absolute Deviation (MAD)243
Skewness0.2397006913
Sum199164143
Variance93745.54671
MonotonicityNot monotonic
2022-08-07T18:52:41.254759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22426982
 
6.6%
2686268
 
1.5%
8985375
 
1.3%
10054666
 
1.1%
764091
 
1.0%
8433900
 
1.0%
2313893
 
1.0%
3283141
 
0.8%
3902997
 
0.7%
4542914
 
0.7%
Other values (1031)342204
84.2%
ValueCountFrequency (%)
01942
0.5%
1896
0.2%
2780
0.2%
3976
0.2%
43
 
< 0.1%
546
 
< 0.1%
61305
0.3%
733
 
< 0.1%
8189
 
< 0.1%
962
 
< 0.1%
ValueCountFrequency (%)
10402
 
< 0.1%
103929
 
< 0.1%
1038342
 
0.1%
103775
 
< 0.1%
1036933
0.2%
10351000
0.2%
10342051
0.5%
10331358
0.3%
1032487
 
0.1%
103128
 
< 0.1%

DERIVADA_FISCALIA
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.742785368
Minimum0
Maximum8
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:41.351757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median4
Q35
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.367427868
Coefficient of variation (CV)0.2883174678
Kurtosis-0.5011545559
Mean4.742785368
Median Absolute Deviation (MAD)0
Skewness1.02380823
Sum1927615
Variance1.869858973
MonotonicityNot monotonic
2022-08-07T18:52:41.428731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4280447
69.0%
792706
 
22.8%
510855
 
2.7%
310838
 
2.7%
86802
 
1.7%
23210
 
0.8%
61538
 
0.4%
132
 
< 0.1%
03
 
< 0.1%
ValueCountFrequency (%)
03
 
< 0.1%
132
 
< 0.1%
23210
 
0.8%
310838
 
2.7%
4280447
69.0%
510855
 
2.7%
61538
 
0.4%
792706
 
22.8%
86802
 
1.7%
ValueCountFrequency (%)
86802
 
1.7%
792706
 
22.8%
61538
 
0.4%
510855
 
2.7%
4280447
69.0%
310838
 
2.7%
23210
 
0.8%
132
 
< 0.1%
03
 
< 0.1%

DIRECCION
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1627
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean762.1546954
Minimum0
Maximum1626
Zeros80
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:41.533760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile58
Q1308
median755
Q31173
95-th percentile1532
Maximum1626
Range1626
Interquartile range (IQR)865

Descriptive statistics

Standard deviation485.2204308
Coefficient of variation (CV)0.6366429725
Kurtosis-1.26936693
Mean762.1546954
Median Absolute Deviation (MAD)426
Skewness0.107796902
Sum309763295
Variance235438.8664
MonotonicityNot monotonic
2022-08-07T18:52:41.649757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
454519
 
1.1%
14693949
 
1.0%
2193893
 
1.0%
4963861
 
0.9%
9233823
 
0.9%
10613743
 
0.9%
5393731
 
0.9%
6582962
 
0.7%
2892961
 
0.7%
1142865
 
0.7%
Other values (1617)370124
91.1%
ValueCountFrequency (%)
080
 
< 0.1%
1209
0.1%
2139
< 0.1%
3181
< 0.1%
443
 
< 0.1%
524
 
< 0.1%
6115
< 0.1%
7251
0.1%
8246
0.1%
911
 
< 0.1%
ValueCountFrequency (%)
162658
 
< 0.1%
162593
 
< 0.1%
162447
 
< 0.1%
162323
 
< 0.1%
16222089
0.5%
162176
 
< 0.1%
1620173
 
< 0.1%
161968
 
< 0.1%
1618101
 
< 0.1%
16171
 
< 0.1%

DIST_CIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct791
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.2835758
Minimum0
Maximum790
Zeros2209
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:41.774757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41
Q1156
median357
Q3620
95-th percentile753
Maximum790
Range790
Interquartile range (IQR)464

Descriptive statistics

Standard deviation238.8140497
Coefficient of variation (CV)0.6279893872
Kurtosis-1.355147751
Mean380.2835758
Median Absolute Deviation (MAD)225
Skewness0.08444590628
Sum154559034
Variance57032.15035
MonotonicityNot monotonic
2022-08-07T18:52:41.889760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62015738
 
3.9%
4110607
 
2.6%
35710398
 
2.6%
7810120
 
2.5%
3729350
 
2.3%
1747486
 
1.8%
1377266
 
1.8%
5286808
 
1.7%
7566017
 
1.5%
7556007
 
1.5%
Other values (781)316634
77.9%
ValueCountFrequency (%)
02209
0.5%
114
 
< 0.1%
22593
0.6%
349
 
< 0.1%
4175
 
< 0.1%
562
 
< 0.1%
692
 
< 0.1%
777
 
< 0.1%
84
 
< 0.1%
915
 
< 0.1%
ValueCountFrequency (%)
7902
 
< 0.1%
78927
 
< 0.1%
788119
 
< 0.1%
78762
 
< 0.1%
7861001
0.2%
785771
0.2%
784535
0.1%
783181
 
< 0.1%
782385
 
0.1%
78140
 
< 0.1%

DIST_HECHO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1331
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean660.6852184
Minimum0
Maximum1330
Zeros2129
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:42.010758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74
Q1272
median643
Q31041
95-th percentile1274
Maximum1330
Range1330
Interquartile range (IQR)769

Descriptive statistics

Standard deviation400.9576373
Coefficient of variation (CV)0.6068815015
Kurtosis-1.331823043
Mean660.6852184
Median Absolute Deviation (MAD)398
Skewness0.0009695883441
Sum268522954
Variance160767.0269
MonotonicityNot monotonic
2022-08-07T18:52:42.127758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104115187
 
3.7%
7410377
 
2.6%
1309539
 
2.3%
10538934
 
2.2%
3098296
 
2.0%
6138267
 
2.0%
12746923
 
1.7%
8976891
 
1.7%
6436791
 
1.7%
12756683
 
1.6%
Other values (1321)318543
78.4%
ValueCountFrequency (%)
02129
0.5%
118
 
< 0.1%
22515
0.6%
348
 
< 0.1%
411
 
< 0.1%
512
 
< 0.1%
6163
 
< 0.1%
75
 
< 0.1%
832
 
< 0.1%
956
 
< 0.1%
ValueCountFrequency (%)
133029
 
< 0.1%
1329358
 
0.1%
132873
 
< 0.1%
1327942
0.2%
13268
 
< 0.1%
1325770
0.2%
13243
 
< 0.1%
13235
 
< 0.1%
1322545
0.1%
13211
 
< 0.1%

DPTO_CIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.66510675
Minimum0
Maximum25
Zeros4137
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:42.230760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median14
Q314
95-th percentile22
Maximum25
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.839635837
Coefficient of variation (CV)0.5006071495
Kurtosis-0.4748146703
Mean11.66510675
Median Absolute Deviation (MAD)3
Skewness-0.09890785154
Sum4741061
Variance34.10134671
MonotonicityNot monotonic
2022-08-07T18:52:42.324757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
14150157
36.9%
337812
 
9.3%
2023054
 
5.7%
1218826
 
4.6%
718718
 
4.6%
617907
 
4.4%
1117488
 
4.3%
1317194
 
4.2%
1015576
 
3.8%
114658
 
3.6%
Other values (16)75041
18.5%
ValueCountFrequency (%)
04137
 
1.0%
114658
 
3.6%
26223
 
1.5%
337812
9.3%
45989
 
1.5%
59590
 
2.4%
617907
4.4%
718718
4.6%
81399
 
0.3%
96467
 
1.6%
ValueCountFrequency (%)
253968
 
1.0%
244216
 
1.0%
237426
 
1.8%
225310
 
1.3%
217557
 
1.9%
2023054
5.7%
191344
 
0.3%
184037
 
1.0%
172087
 
0.5%
16167
 
< 0.1%

DPTO_HECHO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.79440299
Minimum0
Maximum26
Zeros4066
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:42.417761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median14
Q314
95-th percentile23
Maximum26
Range26
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.069502702
Coefficient of variation (CV)0.5146087266
Kurtosis-0.3832476422
Mean11.79440299
Median Absolute Deviation (MAD)3
Skewness0.03916860764
Sum4793611
Variance36.83886305
MonotonicityNot monotonic
2022-08-07T18:52:42.514757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
14149665
36.8%
338000
 
9.3%
2123158
 
5.7%
1218791
 
4.6%
718694
 
4.6%
617875
 
4.4%
1117556
 
4.3%
1317375
 
4.3%
1015587
 
3.8%
114758
 
3.6%
Other values (17)74972
18.4%
ValueCountFrequency (%)
04066
 
1.0%
114758
 
3.6%
26169
 
1.5%
338000
9.3%
46051
 
1.5%
59569
 
2.4%
617875
4.4%
718694
4.6%
81405
 
0.3%
96448
 
1.6%
ValueCountFrequency (%)
263979
 
1.0%
254222
 
1.0%
247454
 
1.8%
235349
 
1.3%
227578
 
1.9%
2123158
5.7%
201369
 
0.3%
195
 
< 0.1%
184008
 
1.0%
172088
 
0.5%

EDAD
Real number (ℝ≥0)

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.54480096
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:42.624757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q127
median34
Q343
95-th percentile57
Maximum75
Range57
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.30389395
Coefficient of variation (CV)0.3180182091
Kurtosis0.1629935235
Mean35.54480096
Median Absolute Deviation (MAD)8
Skewness0.7227917953
Sum14446509
Variance127.7780183
MonotonicityNot monotonic
2022-08-07T18:52:42.733759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3127482
 
6.8%
3013452
 
3.3%
3213287
 
3.3%
2813284
 
3.3%
2913165
 
3.2%
3313042
 
3.2%
3413034
 
3.2%
2713014
 
3.2%
2612769
 
3.1%
3512647
 
3.1%
Other values (48)261255
64.3%
ValueCountFrequency (%)
187228
1.8%
198266
2.0%
209364
2.3%
2110213
2.5%
2211032
2.7%
2311754
2.9%
2412183
3.0%
2512280
3.0%
2612769
3.1%
2713014
3.2%
ValueCountFrequency (%)
75261
 
0.1%
74367
0.1%
73378
0.1%
72443
0.1%
71523
0.1%
70586
0.1%
69644
0.2%
68670
0.2%
67783
0.2%
66901
0.2%

EST_CIVIL
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.023583339
Minimum0
Maximum5
Zeros69121
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:42.824728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q34
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.619986378
Coefficient of variation (CV)0.5357836038
Kurtosis-0.6156782731
Mean3.023583339
Median Absolute Deviation (MAD)0
Skewness-1.120524044
Sum1228878
Variance2.624355864
MonotonicityNot monotonic
2022-08-07T18:52:42.899730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4290832
71.6%
069121
 
17.0%
136425
 
9.0%
35425
 
1.3%
23430
 
0.8%
51198
 
0.3%
ValueCountFrequency (%)
069121
 
17.0%
136425
 
9.0%
23430
 
0.8%
35425
 
1.3%
4290832
71.6%
51198
 
0.3%
ValueCountFrequency (%)
51198
 
0.3%
4290832
71.6%
35425
 
1.3%
23430
 
0.8%
136425
 
9.0%
069121
 
17.0%

LIBRO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.07677318
Minimum0
Maximum42
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:42.996727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q118
median25
Q325
95-th percentile25
Maximum42
Range42
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.343138715
Coefficient of variation (CV)0.2420253482
Kurtosis4.669166998
Mean22.07677318
Median Absolute Deviation (MAD)0
Skewness-1.960104817
Sum8972685
Variance28.54913133
MonotonicityNot monotonic
2022-08-07T18:52:43.098728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
25233602
57.5%
1898431
24.2%
2017699
 
4.4%
2217059
 
4.2%
512513
 
3.1%
3012301
 
3.0%
15996
 
1.5%
292384
 
0.6%
261365
 
0.3%
271053
 
0.3%
Other values (33)4028
 
1.0%
ValueCountFrequency (%)
012
 
< 0.1%
15996
1.5%
28
 
< 0.1%
31041
 
0.3%
41
 
< 0.1%
512513
3.1%
6151
 
< 0.1%
713
 
< 0.1%
8701
 
0.2%
92
 
< 0.1%
ValueCountFrequency (%)
422
 
< 0.1%
4111
 
< 0.1%
401
 
< 0.1%
3932
 
< 0.1%
381
 
< 0.1%
3746
 
< 0.1%
361
 
< 0.1%
35143
< 0.1%
341
 
< 0.1%
3353
 
< 0.1%

MODALIDAD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.233463983
Minimum0
Maximum6
Zeros104
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:43.184757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median5
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8056133537
Coefficient of variation (CV)0.1539350144
Kurtosis0.6954187643
Mean5.233463983
Median Absolute Deviation (MAD)1
Skewness-0.8770026305
Sum2127042
Variance0.6490128757
MonotonicityNot monotonic
2022-08-07T18:52:43.255729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6177852
43.8%
5155965
38.4%
464114
 
15.8%
36900
 
1.7%
21453
 
0.4%
0104
 
< 0.1%
143
 
< 0.1%
ValueCountFrequency (%)
0104
 
< 0.1%
143
 
< 0.1%
21453
 
0.4%
36900
 
1.7%
464114
 
15.8%
5155965
38.4%
6177852
43.8%
ValueCountFrequency (%)
6177852
43.8%
5155965
38.4%
464114
 
15.8%
36900
 
1.7%
21453
 
0.4%
143
 
< 0.1%
0104
 
< 0.1%

PROV_CIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct187
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.74086622
Minimum0
Maximum186
Zeros3544
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:43.356757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q149
median106
Q3106
95-th percentile174
Maximum186
Range186
Interquartile range (IQR)57

Descriptive statistics

Standard deviation47.60646711
Coefficient of variation (CV)0.5189232353
Kurtosis-0.7179301081
Mean91.74086622
Median Absolute Deviation (MAD)31
Skewness-0.1525499981
Sum37286332
Variance2266.375711
MonotonicityNot monotonic
2022-08-07T18:52:43.469757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106139054
34.2%
1131641
 
7.8%
2717907
 
4.4%
17413707
 
3.4%
4613542
 
3.3%
14013135
 
3.2%
6110650
 
2.6%
809899
 
2.4%
917504
 
1.8%
1667363
 
1.8%
Other values (177)142029
34.9%
ValueCountFrequency (%)
03544
0.9%
1129
 
< 0.1%
2123
 
< 0.1%
323
 
< 0.1%
4771
 
0.2%
5286
 
0.1%
61884
0.5%
76
 
< 0.1%
8668
 
0.2%
922
 
< 0.1%
ValueCountFrequency (%)
1861092
0.3%
185205
 
0.1%
184599
0.1%
18358
 
< 0.1%
182338
 
0.1%
18126
 
< 0.1%
180876
0.2%
17921
 
< 0.1%
1781233
0.3%
1771429
0.4%

PROV_HECHO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct190
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.48489165
Minimum0
Maximum189
Zeros3493
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:43.590758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q149
median107
Q3107
95-th percentile176
Maximum189
Range189
Interquartile range (IQR)58

Descriptive statistics

Standard deviation48.2700543
Coefficient of variation (CV)0.5219236725
Kurtosis-0.7167220799
Mean92.48489165
Median Absolute Deviation (MAD)31
Skewness-0.1517261272
Sum37588727
Variance2329.998142
MonotonicityNot monotonic
2022-08-07T18:52:43.702760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107138456
34.1%
1131939
 
7.9%
2617875
 
4.4%
17614371
 
3.5%
4613688
 
3.4%
14113205
 
3.2%
6210486
 
2.6%
8110274
 
2.5%
927459
 
1.8%
1687382
 
1.8%
Other values (180)141296
34.8%
ValueCountFrequency (%)
03493
0.9%
1143
 
< 0.1%
2119
 
< 0.1%
322
 
< 0.1%
4773
 
0.2%
5313
 
0.1%
61887
0.5%
75
 
< 0.1%
8681
 
0.2%
922
 
< 0.1%
ValueCountFrequency (%)
1891068
0.3%
188220
 
0.1%
187571
0.1%
18669
 
< 0.1%
185341
 
0.1%
18422
 
< 0.1%
183794
0.2%
1822
 
< 0.1%
18127
 
< 0.1%
1801255
0.3%

REGION
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.38239209
Minimum0
Maximum53
Zeros4806
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:43.826757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q117
median22
Q335
95-th percentile49
Maximum53
Range53
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.84798921
Coefficient of variation (CV)0.5061772416
Kurtosis-0.6346923179
Mean25.38239209
Median Absolute Deviation (MAD)8
Skewness0.5066307048
Sum10316191
Variance165.0708268
MonotonicityNot monotonic
2022-08-07T18:52:43.941757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22105170
25.9%
4439570
 
9.7%
1228304
 
7.0%
2515649
 
3.9%
2013346
 
3.3%
1413118
 
3.2%
2112519
 
3.1%
1911230
 
2.8%
1711198
 
2.8%
1511113
 
2.7%
Other values (44)145214
35.7%
ValueCountFrequency (%)
04806
1.2%
129
 
< 0.1%
22749
 
0.7%
32007
 
0.5%
43607
 
0.9%
51133
 
0.3%
62555
 
0.6%
7775
 
0.2%
82628
 
0.6%
99883
2.4%
ValueCountFrequency (%)
533091
 
0.8%
527467
 
1.8%
516117
 
1.5%
503120
 
0.8%
491386
 
0.3%
487519
 
1.9%
47366
 
0.1%
461069
 
0.3%
452254
 
0.6%
4439570
9.7%

SEXO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
1
327791 
0
78640 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters406431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1327791
80.7%
078640
 
19.3%

Length

2022-08-07T18:52:44.042757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:52:44.133759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1327791
80.7%
078640
 
19.3%

Most occurring characters

ValueCountFrequency (%)
1327791
80.7%
078640
 
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number406431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1327791
80.7%
078640
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common406431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1327791
80.7%
078640
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII406431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1327791
80.7%
078640
 
19.3%

SUB_TIPO
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
1
404831 
0
 
1600

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters406431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1404831
99.6%
01600
 
0.4%

Length

2022-08-07T18:52:44.212758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:52:44.302757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1404831
99.6%
01600
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1404831
99.6%
01600
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number406431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1404831
99.6%
01600
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common406431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1404831
99.6%
01600
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII406431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1404831
99.6%
01600
 
0.4%

TIPO
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
1
262187 
0
144244 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters406431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1262187
64.5%
0144244
35.5%

Length

2022-08-07T18:52:44.379757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:52:44.470762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1262187
64.5%
0144244
35.5%

Most occurring characters

ValueCountFrequency (%)
1262187
64.5%
0144244
35.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number406431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1262187
64.5%
0144244
35.5%

Most occurring scripts

ValueCountFrequency (%)
Common406431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1262187
64.5%
0144244
35.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII406431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1262187
64.5%
0144244
35.5%

TIPO_DENUNCIA
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2
265750 
0
105177 
1
 
18979
3
 
16525

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters406431
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row2
5th row0

Common Values

ValueCountFrequency (%)
2265750
65.4%
0105177
 
25.9%
118979
 
4.7%
316525
 
4.1%

Length

2022-08-07T18:52:44.550757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:52:44.645757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2265750
65.4%
0105177
 
25.9%
118979
 
4.7%
316525
 
4.1%

Most occurring characters

ValueCountFrequency (%)
2265750
65.4%
0105177
 
25.9%
118979
 
4.7%
316525
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number406431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2265750
65.4%
0105177
 
25.9%
118979
 
4.7%
316525
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common406431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2265750
65.4%
0105177
 
25.9%
118979
 
4.7%
316525
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII406431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2265750
65.4%
0105177
 
25.9%
118979
 
4.7%
316525
 
4.1%

UBICACION
Real number (ℝ≥0)

Distinct379092
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189711.8238
Minimum0
Maximum379091
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:44.745757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19212.5
Q195311
median189880
Q3284768.5
95-th percentile359388.5
Maximum379091
Range379091
Interquartile range (IQR)189457.5

Descriptive statistics

Standard deviation108989.4476
Coefficient of variation (CV)0.5745000255
Kurtosis-1.195043607
Mean189711.8238
Median Absolute Deviation (MAD)94735
Skewness-0.005852900989
Sum7.710476628 × 1010
Variance1.187869969 × 1010
MonotonicityNot monotonic
2022-08-07T18:52:44.855759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
329776156
 
< 0.1%
91801141
 
< 0.1%
296772134
 
< 0.1%
335129109
 
< 0.1%
332160104
 
< 0.1%
263772101
 
< 0.1%
27186498
 
< 0.1%
30061898
 
< 0.1%
13795995
 
< 0.1%
392290
 
< 0.1%
Other values (379082)405305
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
3790911
< 0.1%
3790901
< 0.1%
3790892
< 0.1%
3790881
< 0.1%
3790872
< 0.1%
3790861
< 0.1%
3790851
< 0.1%
3790841
< 0.1%
3790831
< 0.1%
3790821
< 0.1%

VIA
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.736004389
Minimum0
Maximum15
Zeros10550
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:44.957727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median10
Q310
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.652851877
Coefficient of variation (CV)0.4721884443
Kurtosis-0.8072955462
Mean7.736004389
Median Absolute Deviation (MAD)0
Skewness-0.7110922454
Sum3144152
Variance13.34332684
MonotonicityNot monotonic
2022-08-07T18:52:45.036730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
10234895
57.8%
262288
 
15.3%
334497
 
8.5%
830260
 
7.4%
010550
 
2.6%
138068
 
2.0%
67644
 
1.9%
156862
 
1.7%
53944
 
1.0%
72850
 
0.7%
Other values (6)4573
 
1.1%
ValueCountFrequency (%)
010550
 
2.6%
11277
 
0.3%
262288
15.3%
334497
8.5%
41212
 
0.3%
53944
 
1.0%
67644
 
1.9%
72850
 
0.7%
830260
7.4%
9169
 
< 0.1%
ValueCountFrequency (%)
156862
 
1.7%
14780
 
0.2%
138068
 
2.0%
12656
 
0.2%
11479
 
0.1%
10234895
57.8%
9169
 
< 0.1%
830260
 
7.4%
72850
 
0.7%
67644
 
1.9%

PAIS_NATAL
Real number (ℝ≥0)

Distinct153
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.0292448
Minimum0
Maximum152
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:45.140727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile106
Q1106
median106
Q3106
95-th percentile106
Maximum152
Range152
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.551754238
Coefficient of variation (CV)0.04292923377
Kurtosis225.8857469
Mean106.0292448
Median Absolute Deviation (MAD)0
Skewness-7.380304206
Sum43093572
Variance20.71846664
MonotonicityNot monotonic
2022-08-07T18:52:45.254730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106403298
99.2%
1471246
 
0.3%
145660
 
0.2%
37200
 
< 0.1%
6105
 
< 0.1%
5188
 
< 0.1%
3076
 
< 0.1%
14452
 
< 0.1%
1850
 
< 0.1%
6049
 
< 0.1%
Other values (143)607
 
0.1%
ValueCountFrequency (%)
05
 
< 0.1%
114
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
6105
< 0.1%
710
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
1521
 
< 0.1%
1513
 
< 0.1%
1501
 
< 0.1%
1491
 
< 0.1%
1481
 
< 0.1%
1471246
0.3%
1461
 
< 0.1%
145660
0.2%
14452
 
< 0.1%
14313
 
< 0.1%

FEC_REGISTRO_ANIO
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2019
184270 
2018
83017 
2017
78792 
2016
60350 
2014
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1625724
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2019184270
45.3%
201883017
20.4%
201778792
19.4%
201660350
 
14.8%
20142
 
< 0.1%

Length

2022-08-07T18:52:45.363727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-07T18:52:45.463758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2019184270
45.3%
201883017
20.4%
201778792
19.4%
201660350
 
14.8%
20142
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2406431
25.0%
0406431
25.0%
1406431
25.0%
9184270
11.3%
883017
 
5.1%
778792
 
4.8%
660350
 
3.7%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1625724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2406431
25.0%
0406431
25.0%
1406431
25.0%
9184270
11.3%
883017
 
5.1%
778792
 
4.8%
660350
 
3.7%
42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common1625724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2406431
25.0%
0406431
25.0%
1406431
25.0%
9184270
11.3%
883017
 
5.1%
778792
 
4.8%
660350
 
3.7%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1625724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2406431
25.0%
0406431
25.0%
1406431
25.0%
9184270
11.3%
883017
 
5.1%
778792
 
4.8%
660350
 
3.7%
42
 
< 0.1%

FEC_REGISTRO_MES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.10687915
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:45.554757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.540079265
Coefficient of variation (CV)0.5796871327
Kurtosis-1.239482606
Mean6.10687915
Median Absolute Deviation (MAD)3
Skewness0.2049469039
Sum2482025
Variance12.5321612
MonotonicityNot monotonic
2022-08-07T18:52:45.632757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
341468
10.2%
140136
9.9%
539766
9.8%
239523
9.7%
439074
9.6%
1133932
8.3%
1233689
8.3%
1031268
7.7%
630505
7.5%
727880
6.9%
Other values (2)49190
12.1%
ValueCountFrequency (%)
140136
9.9%
239523
9.7%
341468
10.2%
439074
9.6%
539766
9.8%
630505
7.5%
727880
6.9%
827787
6.8%
921403
5.3%
1031268
7.7%
ValueCountFrequency (%)
1233689
8.3%
1133932
8.3%
1031268
7.7%
921403
5.3%
827787
6.8%
727880
6.9%
630505
7.5%
539766
9.8%
439074
9.6%
341468
10.2%

FEC_REGISTRO_DIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.73254255
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:45.724757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.755590138
Coefficient of variation (CV)0.5565273453
Kurtosis-1.181069495
Mean15.73254255
Median Absolute Deviation (MAD)8
Skewness0.007484594357
Sum6394193
Variance76.66035867
MonotonicityNot monotonic
2022-08-07T18:52:45.818757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1514020
 
3.4%
1813939
 
3.4%
413838
 
3.4%
1713748
 
3.4%
1613650
 
3.4%
2013598
 
3.3%
1913550
 
3.3%
1113550
 
3.3%
2613508
 
3.3%
2213505
 
3.3%
Other values (21)269525
66.3%
ValueCountFrequency (%)
112621
3.1%
213353
3.3%
313000
3.2%
413838
3.4%
513502
3.3%
613285
3.3%
713249
3.3%
813411
3.3%
913218
3.3%
1012778
3.1%
ValueCountFrequency (%)
318066
2.0%
3011678
2.9%
2912102
3.0%
2813496
3.3%
2713210
3.3%
2613508
3.3%
2513082
3.2%
2412910
3.2%
2313026
3.2%
2213505
3.3%

FEC_REGISTRO_DIA_SEM
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.778978474
Minimum0
Maximum6
Zeros70685
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:45.906757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.011661519
Coefficient of variation (CV)0.7238852473
Kurtosis-1.229345681
Mean2.778978474
Median Absolute Deviation (MAD)2
Skewness0.1445645785
Sum1129463
Variance4.046782069
MonotonicityNot monotonic
2022-08-07T18:52:45.976758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
070685
17.4%
162428
15.4%
260698
14.9%
357709
14.2%
454292
13.4%
652249
12.9%
548370
11.9%
ValueCountFrequency (%)
070685
17.4%
162428
15.4%
260698
14.9%
357709
14.2%
454292
13.4%
548370
11.9%
652249
12.9%
ValueCountFrequency (%)
652249
12.9%
548370
11.9%
454292
13.4%
357709
14.2%
260698
14.9%
162428
15.4%
070685
17.4%

FECHA_HORA_HECHO_ANIO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.952371
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:46.063728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile2016
Q12017
median2018
Q32019
95-th percentile2019
Maximum2019
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.129988597
Coefficient of variation (CV)0.0005599679227
Kurtosis2.191699247
Mean2017.952371
Median Absolute Deviation (MAD)1
Skewness-0.7546777071
Sum820158400
Variance1.27687423
MonotonicityNot monotonic
2022-08-07T18:52:46.148728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2019183883
45.2%
201882180
20.2%
201778611
19.3%
201661079
 
15.0%
2015584
 
0.1%
201433
 
< 0.1%
201312
 
< 0.1%
201211
 
< 0.1%
20118
 
< 0.1%
20077
 
< 0.1%
Other values (11)23
 
< 0.1%
ValueCountFrequency (%)
19902
 
< 0.1%
19971
 
< 0.1%
20001
 
< 0.1%
20011
 
< 0.1%
20022
 
< 0.1%
20031
 
< 0.1%
20044
< 0.1%
20065
< 0.1%
20077
< 0.1%
20082
 
< 0.1%
ValueCountFrequency (%)
2019183883
45.2%
201882180
20.2%
201778611
19.3%
201661079
 
15.0%
2015584
 
0.1%
201433
 
< 0.1%
201312
 
< 0.1%
201211
 
< 0.1%
20118
 
< 0.1%
20102
 
< 0.1%

FECHA_HORA_HECHO_MES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.061358509
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:46.236727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.545933345
Coefficient of variation (CV)0.5850063711
Kurtosis-1.241209798
Mean6.061358509
Median Absolute Deviation (MAD)3
Skewness0.2106225906
Sum2463524
Variance12.57364329
MonotonicityNot monotonic
2022-08-07T18:52:46.316730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
142413
10.4%
342064
10.3%
239471
9.7%
438787
9.5%
538446
9.5%
1134018
8.4%
1232339
8.0%
1031064
7.6%
630625
7.5%
727878
6.9%
Other values (2)49326
12.1%
ValueCountFrequency (%)
142413
10.4%
239471
9.7%
342064
10.3%
438787
9.5%
538446
9.5%
630625
7.5%
727878
6.9%
827137
6.7%
922189
5.5%
1031064
7.6%
ValueCountFrequency (%)
1232339
8.0%
1134018
8.4%
1031064
7.6%
922189
5.5%
827137
6.7%
727878
6.9%
630625
7.5%
538446
9.5%
438787
9.5%
342064
10.3%

FECHA_HORA_HECHO_DIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.53156624
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:46.409728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.785783002
Coefficient of variation (CV)0.5656726993
Kurtosis-1.183793609
Mean15.53156624
Median Absolute Deviation (MAD)8
Skewness0.02010296669
Sum6312510
Variance77.18998295
MonotonicityNot monotonic
2022-08-07T18:52:46.504729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
115192
 
3.7%
1514179
 
3.5%
313926
 
3.4%
1713918
 
3.4%
2513664
 
3.4%
413647
 
3.4%
1813607
 
3.3%
1413599
 
3.3%
1013592
 
3.3%
2013534
 
3.3%
Other values (21)267573
65.8%
ValueCountFrequency (%)
115192
3.7%
213390
3.3%
313926
3.4%
413647
3.4%
513283
3.3%
613230
3.3%
713297
3.3%
813126
3.2%
913017
3.2%
1013592
3.3%
ValueCountFrequency (%)
317084
1.7%
3011571
2.8%
2911668
2.9%
2813090
3.2%
2713114
3.2%
2613199
3.2%
2513664
3.4%
2413114
3.2%
2312619
3.1%
2212770
3.1%

FECHA_HORA_HECHO_DIA_SEM
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.123583093
Minimum0
Maximum6
Zeros62021
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2022-08-07T18:52:46.593727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.109505325
Coefficient of variation (CV)0.6753479137
Kurtosis-1.355535663
Mean3.123583093
Median Absolute Deviation (MAD)2
Skewness-0.05312313012
Sum1269521
Variance4.450012717
MonotonicityNot monotonic
2022-08-07T18:52:46.666728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
679158
19.5%
062021
15.3%
555642
13.7%
154456
13.4%
253689
13.2%
351331
12.6%
450134
12.3%
ValueCountFrequency (%)
062021
15.3%
154456
13.4%
253689
13.2%
351331
12.6%
450134
12.3%
555642
13.7%
679158
19.5%
ValueCountFrequency (%)
679158
19.5%
555642
13.7%
450134
12.3%
351331
12.6%
253689
13.2%
154456
13.4%
062021
15.3%

Interactions

2022-08-07T18:52:33.706757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:39.399226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:44.270193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:48.941224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:54.341196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:59.497228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:04.396227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:09.173611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:14.180762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:19.138572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:23.889662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:29.055646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:33.855652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:38.906764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:44.162727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:48.993728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:54.061727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:58.844728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:03.977765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:08.659735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:13.576758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:18.562740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:23.497729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:28.307759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:33.900761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:39.605226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:44.457227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:49.144202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:54.545232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:59.695230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:04.590227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:09.368587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:14.388765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:19.332572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:24.084655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-08-07T18:50:53.091224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:58.086197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:03.170226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:07.977225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:12.963761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:17.919760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:22.698656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:27.833647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:32.671676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:37.604733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:42.928758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:47.772737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:52.800762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:57.647760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:02.418759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:07.475775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:12.330728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:17.332732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:22.247757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:27.095758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:32.496769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:37.476762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:43.274194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:47.958197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:53.301225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:58.292227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:03.374231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:08.176201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:13.163795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:18.123763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:22.895660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:28.035646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:32.870646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:37.860739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:43.132729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:47.971765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:53.011760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:57.851732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:02.625731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:07.670731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:12.544733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:17.542741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:22.457733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:27.296729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:32.698740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:37.672761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:43.474201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:48.153196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:53.506199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:58.498197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:03.578200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:08.374227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:13.361789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:18.325793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:23.091656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:28.236657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:33.066682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:38.083731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:43.331759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:48.169728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:53.219759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:58.051731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:03.191767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:07.864728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:12.766736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:17.753758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:22.676729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:27.508740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:32.900760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:37.872761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:43.676224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:48.348201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:53.715226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:58.707228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:03.785205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:08.575197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:13.561790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:18.533788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:23.291660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:28.443646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:33.264680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:38.295731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:43.536758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:48.384729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:53.434759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:58.251757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:03.387761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:08.061728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:12.977757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:17.958730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:22.877735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:27.708732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:33.103757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:38.070765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:43.878199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:48.540228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:53.923228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:58.914225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:03.988194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:08.773206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:13.760793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:18.736809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:23.488625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:28.648648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:33.457647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:38.501732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:43.747774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:48.584769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:53.643727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:58.448734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:03.582727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:08.254727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:13.181764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:18.161757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:23.077731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:27.909735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:33.303762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:38.267758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:44.081194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:48.734233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:54.133228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:50:59.123228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:04.193227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:08.973229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:13.968770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:18.940571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:23.687631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:28.851649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:33.654677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:38.706766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:43.955732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:48.783728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:53.855728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:51:58.648758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:03.781762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:08.450729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:13.382728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:18.365765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:23.278737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:28.107728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-07T18:52:33.505731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-08-07T18:52:46.779728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-07T18:52:47.040727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-07T18:52:47.302732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-07T18:52:47.532758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-07T18:52:47.658757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-07T18:52:38.464757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-07T18:52:39.653765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROMODALIDADPROV_CIAPROV_HECHOREGIONSEXOSUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM
037142832524531212321121741762010103078921010620161142016114
13114539781306640425227261410122431341010620161252016114
24317118137665114142812521061072200122996821010620161362016125
359873304507723355422628271211121095641062016136201512265
46644872496847101021418491921901101434733106201614020164110
571545155459211414304255106107221112256480810620161402016140
64514117932857412122542551741762011122454541010620161402016140
745841095141241114603041581599111317832321062016140201512313
89744985755127414143502541061072211122556541010620161402016136
9523413362910561414344256106107221112216656310620161402016114

Last rows

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROMODALIDADPROV_CIAPROV_HECHOREGIONSEXOSUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM
4064219177977011181232431418616616852110020840910106201912311201912311
406422918414767011181232452420416616852110110677910106201912230201912226
406423918414767011181232441020416616852110132902910106201912300201912296
406424918414767011181232450420616616852110135087210106201912160201912156
406425918414767011181232436418616616852110010844710106201912182201912171
4064269184147670111812324564186166168521100980051010620191222620197312
40642791871476701118123245941861661685211007670410106201912263201912252
406428918414767011181232430420616616852110110217010106201912300201912263
406429918714767011181232422418616616852110013703610106201912311201912311
4064309374676714119823243841841701725211001357691010620191290201911250

Duplicate rows

Most frequently occurring

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROMODALIDADPROV_CIAPROV_HECHOREGIONSEXOSUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM# duplicates
3819677133873712493340425611111211121940831010620183190201831754
5312747551131823328426511111211123362711010620167235201672243
922194137350886233431255111131110264222101062019613320196743
97224420713723213132142564646211112262879310620191302201912913
104224485070111812324610186166168521110292222310620168312201682643
14827541006250450883142567980161112604702106201688020168653
154322410615799741414344256106107221112222607101062018411220184863
197423415353045371111224255959617111217727381062016112322016112323
298722728554992514142702541061072211121273721010620183212201832123
308789411372043681414471186106107221110698581010620191243201912433